Multiagent Reinforcement Learning with Neighbor Action Estimation

2026-01-08

arXiv.org

AI Summary

This work addresses the challenge of explicit action exchange in multi-agent reinforcement learning, which often fails to adapt to real-world constraints such as limited communication bandwidth, high latency, or low reliability. To overcome this limitation, the authors propose a lightweight action estimation mechanism that enables each agent to infer the actions of its neighbors solely from local observations, thereby facilitating cooperative policy learning without explicit communication. The approach is compatible with the standard TD3 algorithm and enhances both decentralized decision-making capabilities and system scalability. Experimental results on a dual-arm robotic cooperative grasping task demonstrate that the proposed method significantly improves system robustness and deployment feasibility while reducing dependence on communication infrastructure.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multiagent reinforcement learning, as a prominent intelligent paradigm, enables collaborative decision-making within complex systems. However, existing approaches often rely on explicit action exchange between agents to evaluate action value functions, which is frequently impractical in real-world engineering environments due to communication constraints, latency, energy consumption, and reliability requirements. From an artificial intelligence perspective, this paper proposes an enhanced multiagent reinforcement learning framework that employs action estimation neural networks to infer agent behaviors. By integrating a lightweight action estimation module, each agent infers neighboring agents'behaviors using only locally observable information, enabling collaborative policy learning without explicit action sharing. This approach is fully compatible with standard TD3 algorithms and scalable to larger multiagent systems. At the engineering application level, this framework has been implemented and validated in dual-arm robotic manipulation tasks: two robotic arms collaboratively lift objects. Experimental results demonstrate that this approach significantly enhances the robustness and deployment feasibility of real-world robotic systems while reducing dependence on information infrastructure. Overall, this research advances the development of decentralized multiagent artificial intelligence systems while enabling AI to operate effectively in dynamic, information-constrained real-world environments.

Problem

• Multiagent Reinforcement Learning
• Action Exchange
• Communication Constraints
• Decentralized Systems
• Real-world Robotics

Innovation

• Multiagent Reinforcement Learning
• Action Estimation
• Decentralized Coordination
• TD3 Compatibility
• Robotic Manipulation